Testing model predictions and revealing basin-scale biogeography with whole-community PCR amplicons from GEOTRACES
Testing model predictions and revealing basin-scale biogeography with whole-community PCR amplicons from GEOTRACES
Abstract:
Dedicated sampling campaigns such as JGOFS, CLIVAR, and GEOTRACES have quantified critical oceanic biogeochemical processes on a global scale. Integrating these measurements with microbial community composition data is highly desirable because it would allow hypotheses about biogeographic distributions to be tested or perhaps lead to the discovery of organisms responsible for a particular biogeochemical process. A promising strategy to generate this microbial community composition data comes from high-throughput sequencing of PCR amplicons generated with the 515Y/926R universal 16S/18S primer set. The two key advantages of the 515Y/926R primers are 1) their comprehensiveness - recovering amplicons from the entire cellular microbial community - and 2) their quantitative nature - recovering gene copy abundances as shown previously with microbial community standards. Compared to metagenomes, amplicons additionally have the advantage of more easily detecting rare community members that may be biogeochemically significant (e.g. diazotrophs). In this study, we applied the 515Y/926R primers to DNA from the recently published bioGEOTRACES metagenomic dataset, and use these results to describe microbial community composition across a longitudinal transect of the southern Pacific Ocean from Australia to Tahiti (GEOTRACES section GP13). Using these data, we compared the distributions of diazotrophs predicted by resource-ratio theory with those observed in our amplicons. Additionally, we describe patterns of Prochlorococcus ecotype abundances in the context of increasing iron and nitrogen limitation eastward into the ultraoligotrophic south Pacific gyre. Finally, we explore patterns of heterotrophic microbial community composition across the transect and discuss potential strategies for leveraging these data to construct trait-based models of the heterotrophic microbial community.